AIMC Topic: Aged, 80 and over

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What are the six degree-of-freedom errors of a robotically-machined femoral cavity in total hip arthroplasty and are they clinically important? An in-vitro study.

Medical engineering & physics
Errors during a robot-assisted THA may result in a femoral cavity with position and orientation different than planned. This can lead to a femoral component placement that inaccurately sets a patient's femoral anteversion (FA), femoral offset (FO), a...

Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks.

Physics in medicine and biology
Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 ...

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.

Scientific reports
Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electr...

Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning.

International journal of neural systems
In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The...

Feature selection before EEG classification supports the diagnosis of Alzheimer's disease.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature selection (FS) algorithms are often considered to find relevant/non-redunda...

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

PloS one
Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proacti...

Identifying incipient dementia individuals using machine learning and amyloid imaging.

Neurobiology of aging
Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge to design studies to test emerging disease-modifying therapies. Although amyloid-β protein is the co...

Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data.

BMC medical informatics and decision making
BACKGROUND: Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learn...

Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets.

PloS one
Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complication...